Question 1

What is Cortagent's Agentic RAG?

Accepted Answer

Cortagent's Agentic RAG is a uniquely on-prem, AI-intelligent retrieval + reasoning layer for enterprise endpoints and agent workflows. It doesn't stop at 'retrieve top-k then generate' - it runs multi-step retrieval with memory and reliability controls so answers stay grounded as context evolves, while sensitive knowledge never leaves your environment.

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Question 2

How is it different from a typical RAG stack?

Accepted Answer

Typical stacks treat retrieval as a one-shot similarity search. Cortagent's Agentic RAG is built around a loop: route, retrieve, fuse evidence, and keep context stable across follow-ups. It also includes multi-layer caching and reliability tooling so teams can inspect and improve behavior.

Question 3

Who is this for?

Accepted Answer

Teams that need AI‑reliable answers to questions about their own on‑prem knowledge: internal docs, logs, runbooks, playbooks, postmortems, and policies. This includes support, engineering, DevOps, operations, research, and enablement teams building agent workflows that must stay correct under pressure. Cortagent's Agentic RAG turns those internal sources into dependable internal endpoints and automations so decisions stay grounded, auditable, and secure—without data leaving your environment.

Question 4

What business problems does it solve?

Accepted Answer

Most teams do not have a knowledge problem - they have a reliability problem. Critical answers are buried across on-prem docs, logs, runbooks, and tribal memory, so outcomes depend on who you ask and how much time they have. Cortagent's Agentic RAG uses AI-intelligent retrieval and Cortagent-specific approaches to dig out those buried answers, then grounds decisions in verified evidence. It turns your internal, on-prem sources into a reasoning layer that retrieves the right proof, synthesizes the best next action for the situation, and can power reliable internal endpoints and automations. The result is faster time-to-answer, fewer repeat incidents, and consistent decisions without moving sensitive data out of your environment.

Question 5

What data can we use with it?

Accepted Answer

Any text content you can ingest as documents and knowledge bases: internal docs, product specs, policies, runbooks, FAQs, and research notes. You control what gets ingested and which sources are queried.

Question 6

What is 'swarm retrieval' in Cortagent?

Accepted Answer

For complex questions, the system can generate targeted sub-queries, retrieve results in parallel, then merge and dedupe the evidence. This helps increase recall for multi-part questions while keeping a traceable path to what was retrieved.

Question 7

How does memory work for follow-up questions?

Accepted Answer

Cortagent maintains conversation context signals and can keep short-term memory across turns (and optionally across sessions). This supports follow-ups like 'What about that?' without forcing the user to restate everything.

Question 8

What is KFE (Knowledge Fabric Engine)?

Accepted Answer

KFE is an additional knowledge-access path that supports more structured retrieval (semantic and claim-aware types). It complements chunk retrieval by helping shape knowledge into a form that is easier to reason over.

Question 9

How does Cortagent stay fast?

Accepted Answer

It uses multiple caching layers (fingerprint cache, semantic cache with context isolation, embedding caching, and decomposition caching) to reduce repeated work. The goal is lower latency while keeping reliability constraints intact.

Question 10

How do we observe and debug what it did?

Accepted Answer

Cortagent is built with diagnostics and metrics in mind: retrieval metadata, cache diagnostics, and optional Prometheus metrics. The intent is to make it inspectable so teams can trace issues and improve behavior over time.

Question 11

How do you reduce hallucinations or 'confident guessing'?

Accepted Answer

We bias the system toward grounded behavior: verification hooks, safety validators, and explicit routing/retrieval strategies. When evidence is missing, the system should prefer asking for more context over inventing details.

Question 12

Is Cortagent's Agentic RAG a SaaS?

Accepted Answer

Cortagent is a broader platform; Cortagent's Agentic RAG is only the foundation and ingestion layer of the much larger Cortagent ecosystem. It is an Artificial Intelligence (AI) core runtime layer that can be productized and delivered as a service or deployed in controlled environments depending on your requirements.